Crystal Growth & Design,
Год журнала:
2024,
Номер
24(15), С. 6284 - 6291
Опубликована: Июль 23, 2024
Diarylethenes
(DAEs)
are
an
exciting
class
of
stimulus-responsive
organic
molecules
that
exhibit
electrocyclization
reactions
upon
exposure
to
light,
heat,
or
other
stimuli.
The
rational
design
DAE-based
crystalline
materials
is,
however,
complicated
by
the
presence
DAE
atropisomers,
only
one
which
is
photoactive.
Data
mining
CSD
produced
1349
unique
molecular
structures
were
subsequently
analyzed
according
selected
chemical
and
geometric
attributes.
Additional
analyses
performed
on
1078
dithienylethene
(DTE)
structures-the
largest
subgroup
within
ensemble.
crystal
structure
landscape,
based
Molecular Pharmaceutics,
Год журнала:
2024,
Номер
21(7), С. 3121 - 3143
Опубликована: Май 30, 2024
Environmental
impacts
of
the
industrial
revolution
necessitate
adoption
sustainable
practices
in
all
areas
development.
The
pharmaceutical
industry
faces
increasing
pressure
to
minimize
its
ecological
footprint
due
significant
contribution
environmental
pollution.
Over
past
two
decades,
cocrystals
have
received
immense
popularity
their
ability
optimize
critical
attributes
active
ingredients
and
presented
an
avenue
bring
improved
drug
products
market.
This
review
explores
potential
as
ecofriendly
alternative
traditional
solid
forms,
offering
a
approach
From
reducing
number
required
doses
improving
stability
actives,
from
eliminating
synthetic
operations
using
pharmaceutically
approved
chemicals,
use
continuous
solvent-free
manufacturing
methods
leveraging
published
data
on
safety
toxicology,
cocrystallization
contributes
sustainability
latest
trends
suggest
promising
role
bringing
novel
medicines
market,
which
has
been
further
fuelled
by
recent
guidance
major
regulatory
agencies.
International Journal of Molecular Sciences,
Год журнала:
2024,
Номер
25(22), С. 12045 - 12045
Опубликована: Ноя. 9, 2024
Pharmaceutical
cocrystals
offer
a
versatile
approach
to
enhancing
the
properties
of
drug
compounds,
making
them
an
important
tool
in
formulation
and
development
by
improving
therapeutic
performance
patient
experience
pharmaceutical
products.
The
prediction
involves
using
computational
theoretical
methods
identify
potential
cocrystal
formers
understand
interactions
between
active
ingredient
coformers.
This
process
aims
predict
whether
two
or
more
molecules
can
form
stable
structure
before
performing
experimental
synthesis,
thus
saving
time
resources.
In
this
review,
commonly
used
are
first
overviewed
then
evaluated
based
on
three
criteria:
efficiency,
cost-effectiveness,
user-friendliness.
Based
these
considerations,
we
suggest
researchers
without
strong
experiences
which
tools
should
be
tested
as
step
workflow
rational
design
cocrystals.
However,
optimal
choice
depends
specific
needs
resources,
combining
from
different
categories
powerful
approach.
Crystal Growth & Design,
Год журнала:
2025,
Номер
25(5), С. 1688 - 1707
Опубликована: Фев. 14, 2025
This
study
investigates
the
cocrystallization
of
griseofulvin
with
phenolic
coformers,
highlighting
its
feasibility
and
variability.
In
addition
to
previously
reported
cocrystal
4-t-butylphenol
(1:1),
experimental
screening
identified
three
new
cocrystals:
phenol
(2:5),
4-t-amylphenol
2,4,6-trichlorophenol
(2:3).
Phenols
carbon
substituents
in
ortho
or
meta
positions
failed
form
cocrystals,
likely
due
steric
hindrance
electron-donating
effects.
contrast,
phenols
chlorine
substituents,
particularly
para
positions,
demonstrated
enhanced
potential,
driven
by
electron-withdrawing
effects
that
promote
hydrogen
bonding.
The
2:5
required
optimized
conditions
for
isolation
exhibited
instability
under
ambient
coformer
sublimation,
a
tendency
also
observed
other
cocrystals.
While
challenging,
sublimation
facilitated
determination
stoichiometric
ratios,
which
varied
from
1:1
2:3
2:5.
Furthermore,
this
provides
data
set
cocrystal-forming
noncocrystal-forming
combinations
as
rigorous
test
case
virtual
prediction.
Among
tested
methods,
crystal
structure
prediction
proved
most
reliable,
identifying
all
and,
together
powder
X-ray
diffraction,
offering
insights
into
structures.
Future
integration
CSP
machine
learning
could
accelerate
speed
accommodate
broader
range
ratios.
Overall,
work
highlights
complexity
potential
cocrystallization.
Journal of Chemical Information and Modeling,
Год журнала:
2025,
Номер
unknown
Опубликована: Март 11, 2025
Drug
cocrystallization
is
a
powerful
strategy
to
enhance
drug
properties
by
modifying
their
physicochemical
characteristics
without
altering
chemical
structure.
However,
the
identification
of
suitable
coformers
remains
challenging
and
resource-intensive
task.
To
streamline
this
process,
we
developed
novel
cocrystal
prediction
model,
Cocry-pred,
which
utilizes
Network-Based
Inference
(NBI)
algorithm─a
dynamic
resource
propagation
method─to
recommend
for
target
molecules
based
on
topological
data
from
network
molecular
substructure
information.
We
evaluated
impact
13
types
fingerprints
different
numbers
rounds
model
performance.
Additionally,
achieve
optimal
performance,
introduced
three
key
hyperparameters─α
(node
weights),
β
(edge
weights)
γ
(penalty
high-degree
nodes)─to
balance
influence
various
factors
within
composite
network.
The
best
performance
Cocry-pred
achieved
an
impressive
AUC
0.885
RS
0.108.
validate
reliability
employed
it
predict
potential
Apatinib.
Subsequently,
seven
Apatinib
cocrystals
were
then
synthesized
experimentally,
among
single-crystal
structures
obtained
two
cocrystals.
This
advancement
highlights
as
tool,
offering
significant
improvements
in
efficiency
providing
valuable
insights
screening
design.
Journal of Computational Chemistry,
Год журнала:
2024,
Номер
45(29), С. 2465 - 2475
Опубликована: Июль 3, 2024
Abstract
Cocrystals
are
assemblies
of
more
than
one
type
molecule
stabilized
through
noncovalent
interactions.
They
promising
materials
for
improved
drug
formulation
in
which
the
stability,
solubility,
or
biocompatibility
active
pharmaceutical
ingredient
(API)
is
by
including
a
coformer.
In
this
work,
range
density
functional
theory
(DFT)
and
tight
binding
(DFTB)
models
systematically
compared
their
ability
to
predict
lattice
enthalpy
broad
existing
pharmaceutically
relevant
cocrystals.
These
from
cocrystals
containing
model
compounds
4,4′‐bipyridine
oxalic
acid
those
with
well
benchmarked
APIs
aspirin
paracetamol,
all
tested
large
set
alternative
coformers.
For
simple
cocrystals,
there
general
consensus
calculated
different
DFT
models.
API
coformers
predictions
depend
strongly
on
model.
The
significantly
lighter
DFTB
unrealistic
values
even
